Why Python for Data Science

Python is a high level, open source, interpreted language that offers a fantastic approach to
object-oriented programming. Data scientists utilize it as one of the best languages for a
variety of projects and applications. Python has excellent capabilities for working with
mathematical, statistical, and scientific functions. It offers excellent libraries for dealing with applications of data science. Even, people without engineering backgrounds may easily
learn how to use it because of this. Additionally, it is better suited for rapid prototyping.

In this article we will have an overview of the python languages, its features and why python is important for data science.

What is Python Programming Language?

Python is a programming language that is used to create software, mobile apps, websites,
and to examine and store scientific and numerical data. Python was initially used for prototyping programmes, automating repetitive operations, and using such applications in other languages. Due to the clean, simple code and extensive documentation, it is relatively simpler to learn and understand. More exciting than Python can be used to aid experts in data analysis in non-technical domains like business and advertising.

Python and Data Science

Python is undoubtedly the language of data science in the future. For data scientists of all
ages and levels of experience, this language offers a wide range of advantages.
Data analytics is not something everyone can learn online. Many data scientists lack coding
knowledge due to their backgrounds in mathematics or statistics. Even the most technically
challenged programmer may rapidly pick up the essentials because of Python's simple syntax.

Python is used by 69% of data scientists and machine learning engineers, according to a
survey. Python is now a requirement for the majority of data science job postings. According to research, 75% of job advertising for "Data Scientists" mention Python experience. Many specify libraries, such as Pytorch, Pandas, NumPy, and Keras.

Why Python For Data Science?

 Python is more straightforward and simpler to understand:- Python's simplicity
and intuitiveness are among its key benefits, and this is what makes it appealing to
programmers who prefer to focus on results rather than getting lost in code. In
addition to being incredibly readable and simple to learn, Python has a lower
entrance barrier than other programming languages like R, Java, or C++, which need
a specific environment to function.

 Jupyter Notebook:- The Jupyter Notebook, which enables web-based coding and
collaboration with other data scientists, is another reason why data scientists adore
Python. IPython, an interactive command-line terminal for Python, gave birth to
Jupyter Notebook. Since not everyone finds working on the command line to be
convenient, they developed a potent online interface to Python and called it Jupyter
Notebook.  An immensely effective tool for creating and presenting Data Science
projects is the Jupyter Notebook. IT enables you to combine visualization, mathematical calculations, and explanations in one document that contains code and its results.

 Tools and Libraries:- Data scientists can benefit from Python and abundance of open-
source Python libraries, which can perform all of these tasks on their behalf. The best
tools for both data analysis and data visualization are available when you employ
these often updated libraries, such as NumPy, Pandas, MatPlotLib, etc., in your
Python scripts. You don’t have to worry about updating libraries, introducing new
functions, etc. if you are a machine learning and data science enthusiast because
someone else is taking care of that for you. To complete your work, you only need to
use the library.

 Community Support:- Since a large number of individuals use Python for Data
Science, you already have an active community to turn to when you run into
problems. The majority of what they produce is released as open source, so you gain
from it as well. TensorFlow and PyTorch, two of the most well-liked Python libraries
for Data Science and Machine Learning, feature contributions from numerous major
corporations including Google and Facebook.

 Pandas:- For data scientists, Pandas is a crucial tool. Data import, export, indexing,
and manipulation functions come in a wide variety with Pandas. Additionally, it offers
useful data structures like DataFrames, which are composed of rows and columns,
and Series, which is a one-dimensional array, as well as effective techniques for
processing them.

Useful Features of Python Language

 Because it makes use of elegant syntax, the programmes are simpler to read.
 It is an easy language to learn, which makes it simple to get the application to run.
 The extensive standard library and community support.
 Python's interactive mode makes it easy to test codes.
 By adding additional modules that are implemented in other compiled languages like
C++ or C, it is equally easy to extend the code in Python.
 Python is a powerful language that may be integrated into other programmes to
provide a customizable interface.
 enables developers to run the code on platforms including Windows, Mac OS X,
Linux, and UNIX everywhere.
 In a few categories, it is free software. Use, download, and application addition of
Pythons are all free of charge.

Importance of Python

Companies that provide data science consultancy are enabling their team of developers and
data scientists to use Python as a programming language. In a very short amount of time,
Python has become the most prominent and well-known programming language.
Data scientists must manage big data, which is a lot of data. Python has gained popularity
as a big data management tool due to its ease of use and extensive library selection.


Any Data Scientist needs Python as a foundation. You can decide which of the many
reasons to choose this potent programming language will be the key one. Python should
undoubtedly be taken into consideration because of its potential and constant improvement, which will enable you to create incredible products and benefit organizations.
In contrast to the certified python developer course, which is a full python programming
course, python for data science course will cover all the principles needed to perform data
science work.


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